Setup model parameters
source('Core_functions.R')
source('Simulation_functions.R')
source('plotting_functions.R')
TEL = 0.95 # Target Efficacy Level
MTT = 0.05 # Maximum Tolerated Toxicity
N_max = 300 # Maximum number of patients recruited
max_increment = 1 # Maximum dose increment to doses previously unseen
N_trials = 2000
N_batch = 3
Randomisation_p_SOC = 0.2 # proportion randomised to the standard of care dose
starting_dose = 12 # starting dose in adaptive arm
SoC = 8 # Standard of Care (only for the adaptive design)
# function that solves for the beta value based on interpretable parameters
solve_beta = function(alpha_val, v_star, y_star){
beta_val = ( logit(y_star) - alpha_val ) / log2(v_star)
return(beta_val)
}
Priors
#******* Prior point estimates *********
Prior_TED = 12; # prior estimate of the Target Efficacious Dose
Prior_alpha_eff = logit(1/10) # prior estimate of the efficacy with one vial
Prior_beta_eff = solve_beta(alpha_val = Prior_alpha_eff, v_star = Prior_TED, y_star = TEL)
Prior_MTD = 32; # prior estimate of the Maximum Tolerated Dose
Prior_alpha_tox = logit(1/1000) # prior estimate of the toxicity after 1 vial
Prior_beta_tox = solve_beta(alpha_val = Prior_alpha_tox, v_star = Prior_MTD, y_star = MTT)
#******* Prior uncertainty estimates *******
prior_model_params = list(beta_tox = Prior_beta_tox,
beta_tox_sd = .05,
alpha_tox=Prior_alpha_tox,
alpha_tox_sd = 2,
beta_eff=Prior_beta_eff,
beta_eff_sd = .05,
alpha_eff=Prior_alpha_eff,
alpha_eff_sd = 2)
Simulation 1
true_alpha_eff = logit(1/50)
true_TED = 20 #
true_beta_eff = solve_beta(alpha_val = true_alpha_eff, v_star = true_TED, y_star = TEL)
true_alpha_tox = logit(1/500) # toxicity at 1 vial
true_MTD = 8 # simulation truth for the MTD
true_beta_tox = solve_beta(alpha_val = true_alpha_tox, v_star = true_MTD, y_star = MTT)
model_params_true = list(beta_tox = true_beta_tox,
alpha_tox=true_alpha_tox,
beta_eff=true_beta_eff,
alpha_eff=true_alpha_eff)
tic()
writeLines('\nSimulation 1, adaptive design...')
##
## Simulation 1, adaptive design...
Full_Simulation(model_params_true = model_params_true,
prior_model_params = prior_model_params,
N_trials = N_trials,
MTT = MTT, TEL = TEL,
N_max = N_max,
N_batch = N_batch,
max_increment = max_increment,
Randomisation_p_SOC = Randomisation_p_SOC,
sim_title = 'Simulation scenario 1',
FORCE_RERUN=FORCE_RERUN,
N_cores = N_cores,
individ_plots = individ_plots,
design_type = 'Adaptive',
starting_dose = starting_dose,
SoC = SoC)
## [1] "done the trial simulation, now plotting results"
toc()
## 0.497 sec elapsed
tic()
writeLines('\nSimulation 1, 3+3 design...')
##
## Simulation 1, 3+3 design...
Full_Simulation(model_params_true = model_params_true,
prior_model_params = prior_model_params,
N_trials = 10*N_trials,
MTT = MTT, TEL = TEL,
N_max = N_max,
N_batch = 3,
max_increment = max_increment,
Randomisation_p_SOC = 0,
sim_title = 'Simulation scenario 1',
FORCE_RERUN=FORCE_RERUN,
N_cores = N_cores,
individ_plots = individ_plots,
design_type = '3+3',
starting_dose = starting_dose,
SoC = SoC)
## [1] "done the trial simulation, now plotting results"
toc()
## 1.147 sec elapsed
compare_rule_vs_model(sim_title = 'Simulation scenario 1',
model_params_true = model_params_true,
prior_model_params = prior_model_params)
## For the rule-based design, 19% of trials give patient 300 a dose within +/-10% of the true optimal dose
## For the model-based design, 27% of trials give patient 300 a dose within +/-10% of the true optimal dose
Simulation 2
true_alpha_eff = logit(1/20)
true_TED = 8 # simulation truth for the MED
true_beta_eff = solve_beta(alpha_val = true_alpha_eff, v_star = true_TED, y_star = TEL)
true_alpha_tox = logit(1/500) # toxicity at 1 vial
true_MTD = 20 # simulation truth for the MTD
true_beta_tox = solve_beta(alpha_val = true_alpha_tox, v_star = true_MTD, y_star = MTT)
model_params_true = list(beta_tox = true_beta_tox,
alpha_tox=true_alpha_tox,
beta_eff=true_beta_eff,
alpha_eff=true_alpha_eff)
tic()
writeLines('\nSimulation 2....')
##
## Simulation 2....
Full_Simulation(model_params_true = model_params_true,
prior_model_params = prior_model_params,
N_trials = N_trials,
MTT = MTT,
TEL = TEL,
N_max = N_max,
N_batch = N_batch,
max_increment = max_increment,
Randomisation_p_SOC = Randomisation_p_SOC,
sim_title='Simulation scenario 2',
FORCE_RERUN=FORCE_RERUN,
N_cores = N_cores,
individ_plots = individ_plots,
design_type = 'Adaptive',
starting_dose = starting_dose,
SoC = SoC)
## [1] "done the trial simulation, now plotting results"
toc()
## 0.342 sec elapsed
tic()
writeLines('\nSimulation 2, 3+3 design...')
##
## Simulation 2, 3+3 design...
Full_Simulation(model_params_true = model_params_true,
prior_model_params = prior_model_params,
N_trials = 10*N_trials,
MTT = MTT, TEL = TEL,
N_max = N_max,
N_batch = 3,
max_increment = max_increment,
Randomisation_p_SOC = 0,
sim_title = 'Simulation scenario 2',
FORCE_RERUN=FORCE_RERUN,
N_cores = N_cores,
individ_plots = individ_plots,
design_type = '3+3',
starting_dose = starting_dose,
SoC = SoC)
## [1] "done the trial simulation, now plotting results"
toc()
## 1.026 sec elapsed
compare_rule_vs_model(sim_title = 'Simulation scenario 2',
model_params_true = model_params_true,
prior_model_params = prior_model_params)
## For the rule-based design, 35% of trials give patient 300 a dose within +/-10% of the true optimal dose
## For the model-based design, 47% of trials give patient 300 a dose within +/-10% of the true optimal dose
Simulation 3
true_alpha_eff = logit(1/100)
true_TED = 60 # simulation truth for the MED
true_beta_eff = solve_beta(alpha_val = true_alpha_eff, v_star = true_TED, y_star = TEL)
true_alpha_tox = logit(1/1000) # toxicity at 1 vial
true_MTD = 30 # simulation truth for the MTD
true_beta_tox = solve_beta(alpha_val = true_alpha_tox, v_star = true_MTD, y_star = MTT)
model_params_true = list(beta_tox = true_beta_tox,
alpha_tox=true_alpha_tox,
beta_eff=true_beta_eff,
alpha_eff=true_alpha_eff)
tic()
writeLines('\nSimulation 3....')
##
## Simulation 3....
Full_Simulation(model_params_true = model_params_true,
prior_model_params = prior_model_params,
N_trials = N_trials,
MTT = MTT,
TEL = TEL,
N_max = N_max,
N_batch = N_batch,
max_increment = max_increment,
Randomisation_p_SOC = Randomisation_p_SOC,
sim_title='Simulation scenario 3',
FORCE_RERUN=FORCE_RERUN,
N_cores = N_cores,
individ_plots = individ_plots,
design_type = 'Adaptive',
starting_dose = starting_dose,
SoC = SoC)
## [1] "done the trial simulation, now plotting results"
toc()
## 0.323 sec elapsed
tic()
writeLines('\nSimulation 3, 3+3 design...')
##
## Simulation 3, 3+3 design...
Full_Simulation(model_params_true = model_params_true,
prior_model_params = prior_model_params,
N_trials = 10*N_trials,
MTT = MTT, TEL = TEL,
N_max = N_max,
N_batch = 3,
max_increment = max_increment,
Randomisation_p_SOC = 0,
sim_title = 'Simulation scenario 3',
FORCE_RERUN=FORCE_RERUN,
N_cores = N_cores,
individ_plots = individ_plots,
design_type = '3+3',
starting_dose = starting_dose,
SoC = SoC)
## [1] "done the trial simulation, now plotting results"
toc()
## 1.017 sec elapsed
compare_rule_vs_model(sim_title = 'Simulation scenario 3',
model_params_true = model_params_true,
prior_model_params = prior_model_params)
## For the rule-based design, 40% of trials give patient 300 a dose within +/-10% of the true optimal dose
## For the model-based design, 31% of trials give patient 300 a dose within +/-10% of the true optimal dose
Simulation 4
true_alpha_eff = logit(1/100)
true_TED = 30 # simulation truth for the MED
true_beta_eff = solve_beta(alpha_val = true_alpha_eff, v_star = true_TED, y_star = TEL)
true_alpha_tox = logit(1/1000) # toxicity at 1 vial
true_MTD = 60 # simulation truth for the MTD
true_beta_tox = solve_beta(alpha_val = true_alpha_tox, v_star = true_MTD, y_star = MTT)
model_params_true = list(beta_tox = true_beta_tox,
alpha_tox=true_alpha_tox,
beta_eff=true_beta_eff,
alpha_eff=true_alpha_eff)
tic()
writeLines('\nSimulation 4, Adaptive design ...')
##
## Simulation 4, Adaptive design ...
Full_Simulation(model_params_true = model_params_true,
prior_model_params = prior_model_params,
N_trials = N_trials,
MTT = MTT,
TEL = TEL,
N_max = N_max,
N_batch = N_batch,
max_increment = max_increment,
Randomisation_p_SOC = Randomisation_p_SOC,
sim_title='Simulation scenario 4',
FORCE_RERUN=FORCE_RERUN,
N_cores = N_cores,
individ_plots = individ_plots,
design_type = 'Adaptive',
starting_dose = starting_dose,
SoC = SoC,epsilon = 0.01)
## [1] "done the trial simulation, now plotting results"
toc()
## 0.269 sec elapsed
tic()
writeLines('\nSimulation 4, 3+3 design ...')
##
## Simulation 4, 3+3 design ...
Full_Simulation(model_params_true = model_params_true,
prior_model_params = prior_model_params,
N_trials = 10*N_trials,
MTT = MTT, TEL = TEL,
N_max = N_max,
N_batch = 3,
max_increment = max_increment,
Randomisation_p_SOC = Randomisation_p_SOC,
sim_title = 'Simulation scenario 4',
FORCE_RERUN=FORCE_RERUN,
N_cores = N_cores,
individ_plots = individ_plots,
design_type = '3+3',
starting_dose = starting_dose,
SoC = SoC)
## [1] "done the trial simulation, now plotting results"
toc()
## 0.935 sec elapsed
compare_rule_vs_model(sim_title = 'Simulation scenario 4',
model_params_true = model_params_true,
prior_model_params = prior_model_params)
## For the rule-based design, 18% of trials give patient 300 a dose within +/-10% of the true optimal dose
## For the model-based design, 72% of trials give patient 300 a dose within +/-10% of the true optimal dose